Reliability

Reliability Engineering is engineering that emphasises dependability in the lifecycle management of a product. Dependability, or reliability, describes the ability of a system or component to function under stated conditions for a specified period of time.

K2 RCM provides courteous, professional, expert and customer focused Reliability engineers and practitioners that are able to apply their extensive skills, knowledge and expertise across a wide range of assets, equipment and industries.

Under the guidance of the FAA, extensive engineering studies were conducted and led by Nowlan and Heap on all of the aircraft in service to determine the source of failures. United Airlines pioneered and published a report on the failures (‘The Nowlan & Heap Report‘) which turned the industry on its head. The concluded that only 9% of the failures were related to the age of the aircraft. The rest were random in nature or induced by the very maintenance work that was put in place to prevent them.

As a result of these findings, many of the extensive maintenance programmes were reduced and the reliability of the aircraft went up!

How Equipment Fails

The report from United Airlines highlighted six unique failure patterns of equipment. Understanding these patterns illustrates why the reduction in maintenance could result in improved performance.

Failure Pattern A is known as the bathtub curve and has a high probability of failure when the equipment is new, followed by a low level of random failures, and followed by a sharp increase in failures at the end of its life. This pattern accounts for approximately 4% of failures.

Failure Pattern B is known as the wear out curve consists of a low level of random failures, followed by a sharp increase in failures at the end of its life. The pattern accounts for approximately 2% of failures.

Failure Pattern C is known as the fatigue curve and is characterised by a gradually increasing level of failures over the course of the equipment’s life. This pattern accounts for approximately 5% of failures.

Failure Pattern D is known as the initial break in curve and starts off with a very low level of failure followed by a sharp rise to a constant level. This pattern accounts for approximately 7% of failures.

Failure Pattern E is known as the random pattern and is a consistent level of random failures over the life of the equipment with no pronounced increases or decreased related to the life of the equipment. This pattern accounts for approximate 11% of failures.

Failure Pattern F is known as the infant mortality curve and shows a high initial failure rate followed by a random level of failures. This pattern accounts for 68% of failures.

What These Patterns Tell Us

When looking at the failure patterns, the first three can be grouped together as the equipment having a defined life, in which the failure rates increase once the equipment has reached a certain age. This age may be time or usage such as hours, cycles, widgets produced, kilometres run, etc. The failures are usually related to the wear, erosion or corrosion and are often simple components which come into contact with the product. The total of these time based failures only account for 9% of all failures.

The other patterns highlight the fact the during the initial start-up of the equipment is when the majority of failure will occur. This could be due to maintenance induced failures, or manufacturing defects in the components. Once the initial start-up period has passed the failure are random. These patterns account for 86% of failure.

Now, these patterns state that the failures are random in nature, but that does not mean that they failures cannot be predicted or mitigated. It means that overhauls, and replacements conducted at a specific frequency are only effective in 9% of the cases.

In the rest of the failures, the equipment can be monitored and the right time to conduct a replacement, or overhaul is identified based on the condition of the equipment. This is known as Condition Based Maintenance, or Predictive Maintenance or even Don’t Fix It Unless It’s Broke!

P to F Curve

The P-F curve has become an essential component to any reliability centred maintenance programme, and being able to understand it can help extend the lifespan of your machines by more than you might think.

Machines are never built to run forever, but they can last a lot longer than one may expect. Machine maintenance is an essential component to any industrial operation, but all too often, these processes are neglected, if not ignored entirely. While it may seem like a hassle or too expensive, having an RCM programme in place can do wonders for the lifespan of your machines and potentially avoid massive costs.

In order to run an effective reliability programme, you need to take the right approach when it comes to making sure that machines are running properly. There are a host of tools available today that can help you detect machine issues before they become real problems, such as vibration sensors, flow meters, ultrasound, audio, thermography, eddy current, X-ray and infrared imaging. But in order for you to properly employ these tools, you need to have a thorough understanding of how machines break down in the first place.

The P-F Curve

The P-F Curve chart is one of the most important tools for a reliability centred maintenance plan. It demonstrates the relationship between machine breakdown, cost, and how it can be prevented. Despite its usefulness a lot of companies may overlook its value.

Simply because a machine is working now, does not mean that failures are not already beginning to occur within the system. In fact, most of the early signals in a machine cannot be detected without the aforementioned tools. By closely examining the P-F curve, we can gain a better understanding of just how essential and cost saving RCM truly is. Let’s examine the various elements of the chart and why they justify a thorough RCM programme.

Equipment condition and time

Machines can still run after a failure has begun, but once this incident occurs, it is only a matter of time before the machine fails entirely.

Along the X-axis of the P-F curve is time. At the start of the axis is when failure starts to occur; at the end of the axis is when the machine actually fails. Along the X-axis there are a number of instances during which the faults can be detected before the point of failure, but unfortunately, the ones that are most notable without the assistance of high-tech tools, usually already signal costly repairs.

Running along the Y-axis is the machine’s condition. Just before and at the point of failure, the assumption is that the machine is already in top working condition. This would put our P-F curve at the top left of the graph. As time progresses from the point of failure, the equipment’s condition moves down the y-axis until it physically fails.

Detecting problems before they happen

The two axes create a plane on which our P-F curve lies, arcing downward on the Y-axis as it moves along the X-axis. It can be difficult to pinpoint when exactly a failure mode has begun, but fortunately, we can look for signs of failure to address this issue before it fails outright. Unfortunately, when signs of failure become most notable, such as audible noise and the machine being hot to touch, it may already be too late.

Early signals of failure are where we need to focus so that we can keep machines running at full capacity and minimise downtime. By using various levels of technology we can detect issues in rotating assets and lubrication that may be causing failures and mechanical inefficiencies. Through these technologies we can also detect electrical faults and other issues.

Understanding Cost

Almost serving as a mirror image to the condition of our machine is the cost of repairing it. When failure starts to occur, the machine is still running relatively well. But as these detection signs begin to crop up, the cost of repair begins to increase as well. If caught early enough, the cost of repair may be a bit of machine downtime needed to lubricate the machine, but as it progresses along the time axis, it becomes more and more expensive, ultimately hitting its peak in cost when the machine fails.

This is why RCM is so important. Because the early detection signs are not noticeable without the aid of technology, we need to be ever-vigilant in making sure that we can detect these issues before they occur. This means conducting routine inspections along designated routes to make sure that no machine gets neglected. Also, tagging issues as they are detected can help provide a visual means as to the condition of a machine.

At the end of the day, RCM simply makes fiscal sense. But in order to make sure that these programmes are operating effectively, and understood by all members of your team, having an understanding of the P-F Curve can help ensure everyone is on the same page.

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